# Lecture22 - CS440/ECE448 Intro to Articial Intelligence...

This preview shows pages 1–12. Sign up to view the full content.

Lecture 21: Classifcation; Decision Trees Prof. Julia Hockenmaier [email protected] http://cs.illinois.edu/fa11/cs440 CS440/ECE448: Intro to ArtiFcial Intelligence

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Supervised learning: classifcation
Supervised learning Given a set D of N items x i , each paired with an output value y i = f( x i ) , discover a function h( x ) which approximates f( x ) D = {( x 1 , y 1 ),… ( x N , y N )} Typically, the input values x are (real-valued or boolean) vectors : x i ˥ R n or x i {0,1} n The output values y are either boolean (binary classifcation) , elements of a Fnite set (multiclass classifcation) , or real (regression)

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
The Naïve Bayes Classifer Each item has a number of attributes A 1 =a 1 ,…,A n =a n We predict the class c based on c = argmax c ! i P(A i = a i | C=c) P(C=c) 4 CS440/ECE448: Intro AI C A1 A2 An
An example Can you train a Naïve Bayes classifer to predict whether the customer wants sugar or not? What is P(coFFee | sugar)? 5 CS440/ECE448: Intro AI x1 x2 Y A1: drink A2: milk? C: sugar? coFFee no yes coFFee yes no tea yes yes tea no no

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Decision trees
Decision trees In this example, the attributes (drink; milk?) are not conditionally independent given the class ( ʻ sugar ʼ ) 7 CS440/ECE448: Intro AI drink? milk? milk? coffee tea yes no no sugar sugar yes no sugar no sugar

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
What is a decision tree? Test 2 Test 6 Test 5 Test 3 Test 4 V11 V22 V21 V12 V13 Label 2 Label 1 Label 1 test 1
Suppose I like circles that are red (I might not be aware of the rule) Features: Owner: John, Mary, Sam Size: Large, Small Shape : Triangle, Circle, Square Texture: Rough, Smooth Color: Blue, Red, Green, Yellow, Taupe Shape Triangle Circle Square Blue Red Green Yellow Taupe Color + - - - - - - ! x [Like(x) " (Circle(x) # Red(x))]

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Suppose I like circles that are red and triangles that are smooth Shape Triangle Circle Square Blue Red Green Yellow Taupe Color + - - - - - - ! x [Like(x) " ((Circle(x) # Red(x) v (Triangle(x) # Smooth(x))] texture smooth rough +
Expressiveness of decision trees Consider binary classifcation (y= true,false ) where the items have Boolean attributes. In the decision tree, each

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

## This note was uploaded on 10/13/2011 for the course CS 440 taught by Professor Levinson,s during the Spring '08 term at University of Illinois, Urbana Champaign.

### Page1 / 38

Lecture22 - CS440/ECE448 Intro to Articial Intelligence...

This preview shows document pages 1 - 12. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online